##Create .log file
replog <- file("SPL_ReplicationCode_Log.log") 
sink(replog, append = TRUE, type = "output")
sink(replog, append = TRUE, type = "message")
cat(readChar(rstudioapi::getSourceEditorContext()$path, 
             file.info(rstudioapi::getSourceEditorContext()$path)$size))

########################################
########################################
########################################
########################################
##Working directory should be set to location of this R file.
########################################
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########################################

########################################
########################################
########################################
########################################
##Replication code for statistical analysis reported in "Complementary Partners? Attitudes toward Multi-Actor Development Projects in the Democratic Republic of Congo"
##Austin Strange, Elizabeth Plantan, and Wendy Leutert
##Last updated: 8 August 2023
########################################
########################################
########################################
########################################
 
##Load packages
library(arsenal)
library(broom)
library(cobalt)
library(dplyr)
library(ggplot2)
library(MASS)
library(plyr)
library(stargazer)
library(tidyr)
library(vtable)
library(xtable)

########################################
########################################
########################################
########################################
##Load, merge, and clean data
########################################
########################################
########################################
######################################## 

##Read cleaned data containing variables used in manuscript analysis (including manuscript and supporting materials)

##Full dataset used for primary analysis
d <- read.csv("SPL_4August23.csv")

##Update treatment groupings into factors with appropriate levels
d$TreatGroup <- as.factor(d$TreatGroup)
d$TreatGroup <- relevel(d$TreatGroup, ref = "CooperationControl")
d$brand_pooled <- as.factor(d$brand_pooled)
d$brand_pooled <- relevel(d$brand_pooled, ref = "Coop")
d$ext_pooled <- as.factor(d$ext_pooled)
d$ext_pooled <- relevel(d$ext_pooled, ref = "NoExt")

##Subsetted dataset of respondents in cooperation (multi-actor) treatment groups
dext <- read.csv("SPL_Cooperation_4August23.csv")

##Subsetted dataset of respondents in cooperation (multi-actor) treatment group with negative externality
dextneg <- read.csv("SPL_CooperationNeg_4August23.csv")

##Subsetted dataset of respondents in treatments group with no externalities
d.noext <- read.csv("SPL_NoExt_4August23.csv")

##Update treatment groupings into factors with appropriate levels
d.noext$TreatGroup <- as.factor(d.noext$TreatGroup)
d.noext$TreatGroup <- relevel(d.noext$TreatGroup, ref = "CooperationControl")

########################################
########################################
########################################
########################################
##Manuscript figures and tables 
########################################
########################################
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########################################

########################################
########################################
##Table 1: Satisfaction levels and difference-in-means 
########################################
########################################

#Compare differences in means by treatment group
t1a <- tidy(t.test(d$PosTre6_Brand_Satisfn[d$TreatGroup == "CooperationControl"], d$PosTre6_Brand_Satisfn[d$TreatGroup == "INGOControl"], data = d))
t1b <- tidy(t.test(d$PosTre6_Brand_Satisfn[d$TreatGroup == "CooperationControl"], d$PosTre6_Brand_Satisfn[d$TreatGroup == "ChinaControl"], data = d))
t1c <- tidy(t.test(d$PosTre6_Brand_Satisfn[d$TreatGroup == "CooperationPositive"], d$PosTre6_Brand_Satisfn[d$TreatGroup == "INGOPositive"], data = d))
t1d <- tidy(t.test(d$PosTre6_Brand_Satisfn[d$TreatGroup == "CooperationPositive"], d$PosTre6_Brand_Satisfn[d$TreatGroup == "ChinaPositive"], data = d))
t1e <- tidy(t.test(d$PosTre6_Brand_Satisfn[d$TreatGroup == "CooperationNegative"], d$PosTre6_Brand_Satisfn[d$TreatGroup == "INGONegative"], data = d))
t1f <- tidy(t.test(d$PosTre6_Brand_Satisfn[d$TreatGroup == "CooperationNegative"], d$PosTre6_Brand_Satisfn[d$TreatGroup == "ChinaNegative"], data = d))

#Store outputs needed to produce Table 1, and produce equivalent table
#Treatment group sizes
ns <- d %>%                           
  group_by(TreatGroup) %>% 
  dplyr::summarize(count = n())

#Mean satisfaction levels
ms <- aggregate(d$PosTre6_Brand_Satisfn, list(d$TreatGroup), FUN=mean) 

#Differences in means and p-values
t1 <- cbind(ns,ms); t1 <- t1[,c(1:2,4)]; colnames(t1)[2:3] <- c("N","Satisfaction Level"); t1$DiffMeans <- ""; t1$pvalue <- ""

#Remove baseline group not used for this hypothesis testing; rearrange treatment groups in line w/ Table 1
t1 <- t1[t1$TreatGroup != "Control_Control",]; t1 <- t1[c(1,7,2,6,9,4,5,8,3),]

#Incorporate differences in means and p-values
t1$DiffMeans[c(2:3,5:6,8:9)] <- c(t1a$estimate,t1b$estimate,t1c$estimate,t1d$estimate,t1e$estimate,t1f$estimate)
t1$pvalue[c(2:3,5:6,8:9)] <- c(t1a$p.value,t1b$p.value,t1c$p.value,t1d$p.value,t1e$p.value,t1f$p.value)

#Print equivalent of Table 1
print(xtable(t1))

########################################
########################################
##Table 2: Attribution and difference-in-means 
########################################
########################################

#Compare differences in means for attribution of blame (in negative externality scenario) to INGO and Chinese SOE
t2a <- tidy(t.test(dextneg$PosTre6_Ext_RespPrim_INGO, dextneg$PosTre6_Ext_RespPrim_ChinaSOE, data = dextneg))

#Compare differences in means for attribution of responsibility (in negative externality scenario) to INGO and Chinese SOE
t2b <- tidy(t.test(dext$PosTre6_Ext_RespSol_INGO[dext$ext_pooled == "Neg"], dext$PosTre6_Ext_RespSol_ChinaSOE[dext$ext_pooled == "Neg"], data = dext)) 

#Compare differences in means for attribution of responsibility (in positive externality scenario) to INGO and Chinese SOE
t2c <- tidy(t.test(dext$PosTre6_Ext_RespScs_INGO[dext$ext_pooled == "Pos"], dext$PosTre6_Ext_RespScs_ChinaSOE[dext$ext_pooled == "Pos"], data = dext))

#Store outputs needed to produce Table 2, and produce equivalent table
#Mean attribution 
t2 <- rbind(t2a,t2b,t2c); t2 <- t2[,c(2,3,1,5)]; colnames(t2) <- c("INGO","Chinese SOE","DiffMeans","pvalue")

#Print equivalent of Table 2
print(xtable(t2))

########################################
########################################
##Table 3: Perceived positive impact/effectiveness level difference-in-means 
########################################
########################################

#Compare differences in means by treatment group, evaluation of INGO
t3a <- tidy(t.test(dext$PosTre6_Brand_Eff_EuroNGO[dext$TreatGroup == "CooperationPositive"], dext$PosTre6_Brand_Eff_EuroNGO[dext$TreatGroup == "CooperationControl"], data = dext)) 
t3b <- tidy(t.test(dext$PosTre6_Brand_Eff_EuroNGO[dext$TreatGroup == "CooperationNegative"],dext$PosTre6_Brand_Eff_EuroNGO[dext$TreatGroup == "CooperationControl"], data = dext)) 

#Compare differences in means by treatment group, evaluation of Chinese SOE
t3c <- tidy(t.test(dext$PosTre6_Brand_Eff_ChinaSOE[dext$TreatGroup == "CooperationPositive"],dext$PosTre6_Brand_Eff_ChinaSOE[dext$TreatGroup == "CooperationControl"], data = dext)) 
t3d <- tidy(t.test(dext$PosTre6_Brand_Eff_ChinaSOE[dext$TreatGroup == "CooperationNegative"], dext$PosTre6_Brand_Eff_ChinaSOE[dext$TreatGroup == "CooperationControl"], data = dext))

#Store outputs needed to produce Table 3, and produce equivalent table
#Mean impact levels
e_ingo <- aggregate(dext$PosTre6_Brand_Eff_EuroNGO, list(dext$TreatGroup), FUN=mean,na.rm=TRUE); e_ingo <- e_ingo[c(1,3,2),] 
e_chn <- aggregate(dext$PosTre6_Brand_Eff_ChinaSOE, list(dext$TreatGroup), FUN=mean,na.rm=TRUE); e_chn <- e_chn[c(1,3,2),]  
t3 <- rbind(e_ingo,e_chn); colnames(t3)[1:2] <- c("Actor/Treatment","Positive Impact"); t3$DiffMeans <- ""; t3$pvalue <- ""

#Incorporate differences in means and p-values
t3$DiffMeans[c(2:3,5:6)] <- c(t3a$estimate,t3b$estimate,t3c$estimate,t3d$estimate)
t3$pvalue[c(2:3,5:6)] <- c(t3a$p.value,t3b$p.value,t3c$p.value,t3d$p.value)

#Print equivalent of Table 3
print(xtable(t3))

########################################
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##Supporting information figures and tables 
########################################
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########################################
########################################

########################################
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##Appendix 3: Descriptive Statistics
########################################
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########################################

########################################
########################################
##Table A1: Summary statistics for regressions
########################################
########################################

#Get statistics for following variables in Table A1
t.a1 <- subset(d, select = c(PosTre6_Brand_Satisfn,sat, BG2_Age, female, Dem3_Edu_if_col, Dem3_Occpn_if_desk, AddOut8_KnowCh, PreT4_Context_PoliKnowle,
                              Knowl9_IMAGINE, act_qrt,PreT4_Eff_INGO,PreT4_Eff_ChinaSOE,AddOut8_WorCovid))
st(t.a1,out='latex')

########################################
########################################
##Table A2: Summary Statistics (Overall and by City)
########################################
########################################

#Get statistics for following variables in Table A2
covsbal <- subset(d, select = c(TreatGroup, BG2_Age,BG2_Sex_1,Dem3_Edu_if_col,Dem3_Occpn_if_desk,Knowl9_IMAGINE,act_qrt,AddOut8_KnowCh,
                                PreT4_Context_PoliKnowle,Dem3_Ownersh_TV,BG2_City))
t.a2 <- tableby(BG2_City ~ ., data = covsbal,test=FALSE)
summary(t.a2, title = "Summary Statistics",text="latex")

########################################
########################################
##Table A3: Balance Table of Core Variables by Treatment Group
########################################
########################################

#Get statistics for following variables in Table A3
t.a3 <- tableby(TreatGroup ~ ., data = covsbal,test=FALSE)
summary(t.a3, title = "Balance Table",text="latex")

########################################
########################################
##Table A4: Pre-treatment Perceptions of Development Actor Effectiveness
########################################
########################################

#Get statistics for following variables in Table A4
pretreat_eff <- subset(d, select = c(PreT4_Eff_WorldBank, PreT4_Eff_DFID, PreT4_Eff_INGO, 
                                     PreT4_Eff_NatiGov, PreT4_Eff_ProvGov, PreT4_Eff_CityGov,
                                     PreT4_Eff_Cong_Pub, PreT4_Eff_EuroComp,PreT4_Eff_ChinaSOE,
                                     PreT4_Eff_CongComp, BG2_City))
t.a4 <- tableby(BG2_City ~ ., data = pretreat_eff)
summary(t.a4, title = "Summary Statistics",text="latex")

########################################
########################################
##Figure A2: Covariate Balance Plots (Figure A3 in conditionally accepted version of manuscript)
########################################
########################################

#Create balance plots for following variables
colnames(covsbal) <- c("Treatment group","Age","Gender","University","Desk job","IMAGINE awareness","IMAGINE neighborhood","Chinese neighbors",
                       "Political knowledge","TV ownership")
covsbal$Gender <- as.character(covsbal$Gender)
covsbal$Female <-ifelse(covsbal$Gender == "Female",1,0)

bp.uni <- bal.plot(covsbal, treat = covsbal$'Treatment group',
                   var.name = "University")
bp.job <- bal.plot(covsbal, treat = covsbal$'Treatment group',
                   var.name = "Desk job")
bp.age <- bal.plot(covsbal, treat = covsbal$'Treatment group',
                   var.name = "Age")
bp.gender <- bal.plot(covsbal, treat = covsbal$'Treatment group',
                      var.name = "Female")
bp.pn <- bal.plot(covsbal, treat = covsbal$'Treatment group',
                  var.name = "Political knowledge")
bp.tv <- bal.plot(covsbal, treat = covsbal$'Treatment group',
                  var.name = "TV ownership")
bp.imag1 <- bal.plot(covsbal, treat = covsbal$'Treatment group',
                     var.name = "IMAGINE awareness")
bp.imag2 <- bal.plot(covsbal, treat = covsbal$'Treatment group',
                     var.name = "IMAGINE neighborhood")

########################################
########################################
##Figures A3 and A4: Reported Reasons for Satisfaction/Dissatisfaction with Project (Figures A6 and A7 in conditionally accepted version of manuscript)
########################################
########################################

#Relevel treatment group factor variable to match figure in manuscript
d.noext$TreatGroup <- relevel(d.noext$TreatGroup, ref = "ChinaControl")

#plot reasons for satisfaction
msat <- d.noext %>%                                       
  group_by(TreatGroup) %>%                         
  summarise_at(vars(sat_qual, sat_health, sat_env, sat_econ, sat_actoreff, sat_integover, sat_citinput),              
               list(name = mean)) 
msat$trt <- msat$TreatGroup; msat <- msat[,c(9,1:8)]
colnames(msat) <- c("Group","TreatGroup","Quality","Health","Environment","Economy","Actor Effectiveness","Integrity","Citizen Input")
msat <- msat %>% gather(Reason, TreatGroup, 'Quality':'Citizen Input'); colnames(msat)[3] <- "Mean"
msat$Mean <- msat$Mean*100
msat$Reason <- factor(msat$Reason, levels=c("Quality", "Health","Environment","Economy","Actor Effectiveness","Integrity","Citizen Input"))
f.a3 <- ggplot(msat, aes(x = Reason, y = Mean, fill = Group)) +
  geom_col(position = "dodge") +
  xlab("Reason") + ylab("Percentage of Respondents") +
  theme(axis.title.x = element_text(size=20),
        axis.text.x  = element_text(size=14, color="black"),
        axis.text.y  = element_text(size=14, color="black"),
        axis.title.y = element_text(size=20),
        legend.title=element_text(size=20),
        legend.text=element_text(size=14))

#plot reasons for dissatisfaction
munsat <- d.noext %>%                                       
  group_by(TreatGroup) %>%                         
  summarise_at(vars(unsat_qual, unsat_health, unsat_env, unsat_econ, unsat_actoreff, unsat_integover, unsat_citinput),              
               list(name = mean)) 
munsat$trt <- munsat$TreatGroup; munsat <- munsat[,c(9,1:8)]
colnames(munsat) <- c("Group","TreatGroup","Quality","Health","Environment","Economy","Actor Effectiveness","Integrity","Citizen Input")
munsat <- munsat %>% gather(Reason, TreatGroup, 'Quality':'Citizen Input'); colnames(munsat)[3] <- "Mean"
munsat$Mean <- munsat$Mean*100
munsat$Reason <- factor(munsat$Reason, levels=c("Quality", "Health","Environment","Economy","Actor Effectiveness","Integrity","Citizen Input"))
f.a4 <- ggplot(munsat, aes(x = Reason, y = Mean, fill = Group)) +
  geom_col(position = "dodge") +
  xlab("Reason") + ylab("Percentage of Respondents") +
  theme(axis.title.x = element_text(size=20),
        axis.text.x  = element_text(size=14, color="black"),
        axis.text.y  = element_text(size=14, color="black"),
        axis.title.y = element_text(size=20),
        legend.title=element_text(size=20),
        legend.text=element_text(size=14))

########################################
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##Appendix 4: Additional Tests
########################################
########################################
########################################

########################################
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##Table A5: Determinants of Project Satisfaction
########################################
########################################

#Run regressions reported in Table A5
reg_pool_1 <- lm(PosTre6_Brand_Satisfn ~ brand_pooled + ext_pooled + brand_pooled*ext_pooled,data=d); summary(reg_pool_1)
reg_pool_2 <- lm(PosTre6_Brand_Satisfn ~ brand_pooled + ext_pooled + brand_pooled*ext_pooled + BG2_Age + BG2_Sex_1 + Dem3_Edu_if_col + 
                   Dem3_Occpn_if_desk + BG2_City + Knowl9_IMAGINE + act_qrt + AddOut8_KnowCh + PreT4_Context_PoliKnowle +
                   BG2_City,data=d)
stargazer(reg_pool_1, reg_pool_2, single.row = T, digits = 2, star.cutoffs = c(.05, .01, .001,.0001))

########################################
########################################
##Table A6: Determinants of Project Satisfaction (with Neighborhood Fixed Effects)
########################################
########################################

#Run regressions reported in Table A6
reg_pool_1fe <- lm(PosTre6_Brand_Satisfn ~ brand_pooled + ext_pooled + brand_pooled*ext_pooled + + factor(BG2_Qrt),data=d)
reg_pool_2fe <- lm(PosTre6_Brand_Satisfn ~ brand_pooled + ext_pooled + brand_pooled*ext_pooled + BG2_Age + BG2_Sex_1 + Dem3_Edu_if_col + 
                     Dem3_Occpn_if_desk + BG2_City + Knowl9_IMAGINE + act_qrt + AddOut8_KnowCh + PreT4_Context_PoliKnowle +
                   factor(BG2_Qrt),data=d)
stargazer(reg_pool_1fe, reg_pool_2fe, single.row = T, digits = 2, star.cutoffs = c(.05, .01, .001,.0001))

########################################
########################################
##Table A7: Determinants of Project Satisfaction (with Enumerator Fixed Effects)
########################################
########################################

#Run regressions reported in Table A7
reg_pool_e1 <- lm(PosTre6_Brand_Satisfn ~ brand_pooled + ext_pooled + brand_pooled*ext_pooled + factor(enumid),data=d)
reg_pool_e2 <- lm(PosTre6_Brand_Satisfn ~ brand_pooled + ext_pooled + brand_pooled*ext_pooled + BG2_Age + BG2_Sex_1 + Dem3_Edu_if_col + 
                    Dem3_Occpn_if_desk + BG2_City + Knowl9_IMAGINE + act_qrt + AddOut8_KnowCh + PreT4_Context_PoliKnowle +
                    factor(enumid),data=d)
stargazer(reg_pool_e1, reg_pool_e2, single.row = T, digits = 2, star.cutoffs = c(.05, .01, .001,.0001))

########################################
########################################
##Table A8: Support for Multi-Actor: Non-Pooled Data
########################################
########################################

#Relevel treatment group factor variable to match figure in manuscript
d.noext$TreatGroup <- relevel(d.noext$TreatGroup, ref = "CooperationControl")

#Run regressions reported in Table A8
h1cdm1 <- glm(sat ~ TreatGroup, data = d.noext, family = binomial(link = "logit")); summary(h1cdm1)
h1cdm2 <- glm(sat ~ TreatGroup + BG2_Age + BG2_Sex_1 + Dem3_Edu_lev + Dem3_Occpn_if_desk + BG2_City + Knowl9_IMAGINE + act_qrt + AddOut8_KnowCh + 
                PreT4_Context_PoliKnowle, data = d.noext, family = binomial(link = "logit")); summary(h1cdm2)
stargazer(h1cdm1, h1cdm2, single.row = T, digits = 2, star.cutoffs = c(.05, .01, .001,.0001))

########################################
########################################
##Table A9: Evaluations of INGO and SOE Effectiveness
########################################
########################################

#Run regressions reported in Table A9
reg_pool_3 <- lm(PosTre6_Brand_Eff_EuroNGO ~ brand_pooled + ext_pooled + brand_pooled*ext_pooled,data=d)

reg_pool_3c <- lm(PosTre6_Brand_Eff_EuroNGO ~ brand_pooled + ext_pooled + brand_pooled*ext_pooled + PreT4_Eff_INGO +
                    BG2_Age + BG2_Sex_1 + Dem3_Edu_if_col + Dem3_Occpn_if_desk + BG2_City + Knowl9_IMAGINE + act_qrt + 
                    AddOut8_KnowCh + PreT4_Context_PoliKnowle,data=d)

reg_pool_4 <- lm(PosTre6_Brand_Eff_ChinaSOE ~ brand_pooled + ext_pooled + brand_pooled*ext_pooled,data=d)

reg_pool_4c <- lm(PosTre6_Brand_Eff_ChinaSOE ~ brand_pooled + ext_pooled + brand_pooled*ext_pooled + PreT4_Eff_ChinaSOE +
                    BG2_Age + BG2_Sex_1 + Dem3_Edu_if_col + Dem3_Occpn_if_desk + BG2_City + Knowl9_IMAGINE + act_qrt + 
                    AddOut8_KnowCh + PreT4_Context_PoliKnowle,data=d)

stargazer(reg_pool_3, reg_pool_3c, reg_pool_4, reg_pool_4c, single.row = T, digits = 2, star.cutoffs = c(.05, .01, .001,.0001))

########################################
########################################
##Table A10: Attribution, Non-Pooled Sample
########################################
########################################

#Run regressions reported in Table A10
h5m1a <- glm(ingo_eff ~ TreatGroup, data = dext, family = binomial(link = "logit"))

h5m2a <- glm(ingo_eff ~ TreatGroup + BG2_Age + BG2_Sex_1 + Dem3_Edu_lev + Dem3_Occpn_if_desk + BG2_City + Knowl9_IMAGINE + act_qrt + AddOut8_KnowCh + 
               PreT4_Context_PoliKnowle, data = dext, family = binomial(link = "logit"))

h5m1b <- glm(soe_eff ~ TreatGroup, data = dext, family = binomial(link = "logit"))

h5m2b <- glm(soe_eff ~ TreatGroup + BG2_Age + BG2_Sex_1 + Dem3_Edu_lev + Dem3_Occpn_if_desk + BG2_City + Knowl9_IMAGINE + act_qrt + AddOut8_KnowCh + 
               PreT4_Context_PoliKnowle, data = dext, family = binomial(link = "logit"))

stargazer(h5m1a, h5m2a, h5m1b, h5m2b, single.row = T, digits = 2, star.cutoffs = c(.05, .01, .001,.0001))

########################################
########################################
##Figure A5: Effect of Moving from Single-Actor to Multi-Actor on Project Satisfaction (Figure A8 in conditionally accepted version of manuscript)
########################################
########################################

#Create effects plot for Figure A5
Externality <- c("Positive","Positive","None","None","Negative","Negative")
Branding <- c("INGO","SOE","INGO","SOE","INGO","SOE")
ATE <- c(.1145,.3049,.3476,.3,.0735,.059)
LCI <- c(-.1781,0.0061,0.06,.01486,-0.2168,-0.248)
HCI <- c(.4072,.6038,.6357,.5851,.3638,.366)
d.ate.plot <- as.data.frame(cbind(Externality,Branding,ATE,LCI,HCI))
d.ate.plot$ATE <- as.numeric(as.character(d.ate.plot$ATE))
d.ate.plot$LCI <- as.numeric(as.character(d.ate.plot$LCI))
d.ate.plot$HCI <- as.numeric(as.character(d.ate.plot$HCI))

f.a5 <- ggplot(d.ate.plot, aes(x=Externality,y=ATE, color=Branding)) +
  geom_point(position=position_dodge(width = .5),size=5,aes(shape=Branding)) +
  geom_errorbar(aes(ymin=LCI, ymax=HCI,color=Branding),position=position_dodge(width = .5),width=.2) +
  ylim(-.3,.85) +
  coord_flip() +
  geom_hline(yintercept=0, linetype="dashed", color = "black") +
  scale_color_manual(values=c("black", "black")) +
  theme(axis.title.x = element_text(size=16),
        axis.text.x  = element_text(size=14,color="black"),
        axis.text.y  = element_text(size=14,color="black"),
        axis.title.y = element_text(size=16),
        legend.title=element_text(size=16),
        legend.text=element_text(size=14),
        legend.key.size =unit(2,'cm')) +
  ylab("Average Treatment Effect Estimate (Raw 1-7 scale)")

##Close .log file
closeAllConnections()
% latex table generated in R 4.1.2 by xtable 1.8-4 package
% Tue Aug  8 09:20:44 2023
\begin{table}[ht]
\centering
\begin{tabular}{rlrrll}
  \hline
 & TreatGroup & N & Satisfaction Level & DiffMeans & pvalue \\ 
  \hline
1 & CooperationControl & 240 & 5.63 &  &  \\ 
  8 & INGOControl & 224 & 5.29 & 0.347619047619048 & 0.0181545349258398 \\ 
  2 & ChinaControl & 231 & 5.33 & 0.300000000000001 & 0.0392448767110906 \\ 
  7 & CooperationPositive & 227 & 5.52 &  &  \\ 
  10 & INGOPositive & 227 & 5.41 & 0.11453744493392 & 0.44217980856147 \\ 
  4 & ChinaPositive & 228 & 5.22 & 0.304930829275833 & 0.0455228796226396 \\ 
  6 & CooperationNegative & 235 & 5.34 &  &  \\ 
  9 & INGONegative & 236 & 5.27 & 0.0734944103858632 & 0.619105914453483 \\ 
  3 & ChinaNegative & 224 & 5.29 & 0.0589665653495439 & 0.705988228262059 \\ 
   \hline
\end{tabular}
\end{table}
% latex table generated in R 4.1.2 by xtable 1.8-4 package
% Tue Aug  8 09:20:44 2023
\begin{table}[ht]
\centering
\begin{tabular}{rrrrr}
  \hline
 & INGO & Chinese SOE & DiffMeans & pvalue \\ 
  \hline
1 & 0.33 & 0.19 & 0.14 & 0.00 \\ 
  2 & 0.34 & 0.12 & 0.22 & 0.00 \\ 
  3 & 0.50 & 0.19 & 0.31 & 0.00 \\ 
   \hline
\end{tabular}
\end{table}
% latex table generated in R 4.1.2 by xtable 1.8-4 package
% Tue Aug  8 09:20:44 2023
\begin{table}[ht]
\centering
\begin{tabular}{rlrll}
  \hline
 & Actor/Treatment & Positive Impact & DiffMeans & pvalue \\ 
  \hline
1 & CooperationControl & 5.28 &  &  \\ 
  3 & CooperationPositive & 5.16 & -0.119900627615062 & 0.381375949841176 \\ 
  2 & CooperationNegative & 4.94 & -0.33649545520127 & 0.0146790007306988 \\ 
  11 & CooperationControl & 5.07 &  &  \\ 
  31 & CooperationPositive & 5.17 & 0.0944907110826394 & 0.503680817928928 \\ 
  21 & CooperationNegative & 4.96 & -0.11038961038961 & 0.423809495864822 \\ 
   \hline
\end{tabular}
\end{table}
\begin{table}[!htbp] \centering \renewcommand*{\arraystretch}{1.1}\caption{Summary Statistics}\resizebox{\textwidth}{!}{
\begin{tabular}{lrrrrrrr}
\hline
\hline
Variable & N & Mean & Std. Dev. & Min & Pctl. 25 & Pctl. 75 & Max \\ 
\hline
PosTre6\_Brand\_Satisfn & 2549 & 5.388 & 1.625 & 1 & 5 & 6 & 7 \\ 
sat & 2549 & 0.763 & 0.426 & 0 & 1 & 1 & 1 \\ 
BG2\_Age & 2549 & 32.435 & 13.294 & 18 & 22 & 39 & 87 \\ 
female & 2549 & 0.433 & 0.496 & 0 & 0 & 1 & 1 \\ 
Dem3\_Edu\_if\_col & 2549 & 0.171 & 0.376 & 0 & 0 & 0 & 1 \\ 
Dem3\_Occpn\_if\_desk & 2549 & 0.167 & 0.373 & 0 & 0 & 0 & 1 \\ 
AddOut8\_KnowCh & 2549 & 0.107 & 0.309 & 0 & 0 & 0 & 1 \\ 
PreT4\_Context\_PoliKnowle & 2549 & 1.934 & 1.217 & 0 & 1 & 3 & 4 \\ 
Knowl9\_IMAGINE & 2549 & 0.262 & 0.44 & 0 & 0 & 1 & 1 \\ 
act\_qrt & 2549 & 0.743 & 0.437 & 0 & 0 & 1 & 1 \\ 
PreT4\_Eff\_INGO & 2220 & 5.214 & 1.607 & 1 & 4 & 6 & 7 \\ 
PreT4\_Eff\_ChinaSOE & 2127 & 4.656 & 1.702 & 1 & 4 & 6 & 7 \\ 
AddOut8\_WorCovid & 2358 & 5.626 & 1.83 & 1 & 5 & 7 & 7\\ 
\hline
\hline
\end{tabular}
}
\end{table}
\begin{table}

\caption{Summary Statistics}
\centering
\begin{tabular}[t]{l|c|c|c}
\hline
 & Bukavu (N=1250) & Goma (N=1299) & Total (N=2549)\\
\hline
\textbf{TreatGroup} &  &  & \\
\hline
~~~CooperationControl & 119 (9.5\%) & 121 (9.3\%) & 240 (9.4\%)\\
\hline
~~~ChinaControl & 117 (9.4\%) & 114 (8.8\%) & 231 (9.1\%)\\
\hline
~~~ChinaNegative & 114 (9.1\%) & 110 (8.5\%) & 224 (8.8\%)\\
\hline
~~~ChinaPositive & 111 (8.9\%) & 117 (9.0\%) & 228 (8.9\%)\\
\hline
~~~Control_Control & 235 (18.8\%) & 242 (18.6\%) & 477 (18.7\%)\\
\hline
~~~CooperationNegative & 112 (9.0\%) & 123 (9.5\%) & 235 (9.2\%)\\
\hline
~~~CooperationPositive & 106 (8.5\%) & 121 (9.3\%) & 227 (8.9\%)\\
\hline
~~~INGOControl & 104 (8.3\%) & 120 (9.2\%) & 224 (8.8\%)\\
\hline
~~~INGONegative & 116 (9.3\%) & 120 (9.2\%) & 236 (9.3\%)\\
\hline
~~~INGOPositive & 116 (9.3\%) & 111 (8.5\%) & 227 (8.9\%)\\
\hline
\textbf{BG2_Age} &  &  & \\
\hline
~~~Mean (SD) & 33.568 (13.942) & 31.346 (12.549) & 32.435 (13.294)\\
\hline
~~~Range & 18.000 - 87.000 & 18.000 - 85.000 & 18.000 - 87.000\\
\hline
\textbf{BG2_Sex_1} &  &  & \\
\hline
~~~Female & 557 (44.6\%) & 546 (42.0\%) & 1103 (43.3\%)\\
\hline
~~~Male & 693 (55.4\%) & 753 (58.0\%) & 1446 (56.7\%)\\
\hline
\textbf{Dem3_Edu_if_col} &  &  & \\
\hline
~~~Mean (SD) & 0.106 (0.308) & 0.232 (0.423) & 0.171 (0.376)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{Dem3_Occpn_if_desk} &  &  & \\
\hline
~~~Mean (SD) & 0.125 (0.331) & 0.208 (0.406) & 0.167 (0.373)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{Knowl9_IMAGINE} &  &  & \\
\hline
~~~Mean (SD) & 0.358 (0.480) & 0.169 (0.375) & 0.262 (0.440)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{act_qrt} &  &  & \\
\hline
~~~Mean (SD) & 0.742 (0.437) & 0.744 (0.437) & 0.743 (0.437)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{AddOut8_KnowCh} &  &  & \\
\hline
~~~Mean (SD) & 0.098 (0.298) & 0.115 (0.319) & 0.107 (0.309)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{PreT4_Context_PoliKnowle} &  &  & \\
\hline
~~~Mean (SD) & 1.723 (1.263) & 2.138 (1.136) & 1.934 (1.217)\\
\hline
~~~Range & 0.000 - 4.000 & 0.000 - 4.000 & 0.000 - 4.000\\
\hline
\textbf{Dem3_Ownersh_TV} &  &  & \\
\hline
~~~Mean (SD) & 0.438 (0.496) & 0.652 (0.477) & 0.547 (0.498)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\end{tabular}
\end{table}

\begin{table}

\caption{Balance Table}
\centering
\begin{tabular}[t]{l|c|c|c|c|c|c|c|c|c|c|c}
\hline
 & CooperationControl (N=240) & ChinaControl (N=231) & ChinaNegative (N=224) & ChinaPositive (N=228) & Control_Control (N=477) & CooperationNegative (N=235) & CooperationPositive (N=227) & INGOControl (N=224) & INGONegative (N=236) & INGOPositive (N=227) & Total (N=2549)\\
\hline
\textbf{BG2_Age} &  &  &  &  &  &  &  &  &  &  & \\
\hline
~~~Mean (SD) & 32.900 (12.562) & 32.143 (13.583) & 31.853 (12.395) & 32.193 (13.464) & 32.237 (12.818) & 32.102 (14.425) & 32.291 (12.863) & 32.978 (13.377) & 32.881 (13.361) & 32.969 (14.609) & 32.435 (13.294)\\
\hline
~~~Range & 18.000 - 87.000 & 18.000 - 80.000 & 18.000 - 80.000 & 18.000 - 78.000 & 18.000 - 85.000 & 18.000 - 86.000 & 18.000 - 77.000 & 18.000 - 87.000 & 18.000 - 82.000 & 18.000 - 83.000 & 18.000 - 87.000\\
\hline
\textbf{BG2_Sex_1} &  &  &  &  &  &  &  &  &  &  & \\
\hline
~~~Female & 109 (45.4\%) & 105 (45.5\%) & 95 (42.4\%) & 103 (45.2\%) & 219 (45.9\%) & 97 (41.3\%) & 92 (40.5\%) & 100 (44.6\%) & 90 (38.1\%) & 93 (41.0\%) & 1103 (43.3\%)\\
\hline
~~~Male & 131 (54.6\%) & 126 (54.5\%) & 129 (57.6\%) & 125 (54.8\%) & 258 (54.1\%) & 138 (58.7\%) & 135 (59.5\%) & 124 (55.4\%) & 146 (61.9\%) & 134 (59.0\%) & 1446 (56.7\%)\\
\hline
\textbf{Dem3_Edu_if_col} &  &  &  &  &  &  &  &  &  &  & \\
\hline
~~~Mean (SD) & 0.179 (0.384) & 0.152 (0.359) & 0.205 (0.405) & 0.175 (0.381) & 0.164 (0.370) & 0.174 (0.380) & 0.137 (0.344) & 0.170 (0.376) & 0.195 (0.397) & 0.163 (0.370) & 0.171 (0.376)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{Dem3_Occpn_if_desk} &  &  &  &  &  &  &  &  &  &  & \\
\hline
~~~Mean (SD) & 0.146 (0.354) & 0.169 (0.375) & 0.165 (0.372) & 0.206 (0.405) & 0.170 (0.376) & 0.170 (0.377) & 0.137 (0.344) & 0.170 (0.376) & 0.169 (0.376) & 0.167 (0.374) & 0.167 (0.373)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{Knowl9_IMAGINE} &  &  &  &  &  &  &  &  &  &  & \\
\hline
~~~Mean (SD) & 0.287 (0.454) & 0.286 (0.453) & 0.250 (0.434) & 0.241 (0.429) & 0.262 (0.440) & 0.264 (0.442) & 0.269 (0.444) & 0.241 (0.429) & 0.280 (0.450) & 0.233 (0.424) & 0.262 (0.440)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{act_qrt} &  &  &  &  &  &  &  &  &  &  & \\
\hline
~~~Mean (SD) & 0.754 (0.431) & 0.749 (0.435) & 0.741 (0.439) & 0.746 (0.436) & 0.736 (0.441) & 0.762 (0.427) & 0.736 (0.442) & 0.741 (0.439) & 0.725 (0.448) & 0.749 (0.435) & 0.743 (0.437)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{AddOut8_KnowCh} &  &  &  &  &  &  &  &  &  &  & \\
\hline
~~~Mean (SD) & 0.104 (0.306) & 0.104 (0.306) & 0.125 (0.331) & 0.105 (0.308) & 0.107 (0.309) & 0.115 (0.320) & 0.123 (0.330) & 0.094 (0.292) & 0.123 (0.329) & 0.066 (0.249) & 0.107 (0.309)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{PreT4_Context_PoliKnowle} &  &  &  &  &  &  &  &  &  &  & \\
\hline
~~~Mean (SD) & 1.925 (1.190) & 1.905 (1.223) & 1.960 (1.247) & 1.974 (1.202) & 1.908 (1.218) & 1.923 (1.248) & 2.004 (1.184) & 1.946 (1.273) & 1.958 (1.173) & 1.872 (1.232) & 1.934 (1.217)\\
\hline
~~~Range & 0.000 - 4.000 & 0.000 - 4.000 & 0.000 - 4.000 & 0.000 - 4.000 & 0.000 - 4.000 & 0.000 - 4.000 & 0.000 - 4.000 & 0.000 - 4.000 & 0.000 - 4.000 & 0.000 - 4.000 & 0.000 - 4.000\\
\hline
\textbf{Dem3_Ownersh_TV} &  &  &  &  &  &  &  &  &  &  & \\
\hline
~~~Mean (SD) & 0.567 (0.497) & 0.545 (0.499) & 0.504 (0.501) & 0.539 (0.500) & 0.581 (0.494) & 0.540 (0.499) & 0.581 (0.494) & 0.562 (0.497) & 0.551 (0.498) & 0.458 (0.499) & 0.547 (0.498)\\
\hline
~~~Range & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000 & 0.000 - 1.000\\
\hline
\textbf{BG2_City} &  &  &  &  &  &  &  &  &  &  & \\
\hline
~~~Bukavu & 119 (49.6\%) & 117 (50.6\%) & 114 (50.9\%) & 111 (48.7\%) & 235 (49.3\%) & 112 (47.7\%) & 106 (46.7\%) & 104 (46.4\%) & 116 (49.2\%) & 116 (51.1\%) & 1250 (49.0\%)\\
\hline
~~~Goma & 121 (50.4\%) & 114 (49.4\%) & 110 (49.1\%) & 117 (51.3\%) & 242 (50.7\%) & 123 (52.3\%) & 121 (53.3\%) & 120 (53.6\%) & 120 (50.8\%) & 111 (48.9\%) & 1299 (51.0\%)\\
\hline
\end{tabular}
\end{table}

\begin{table}

\caption{Summary Statistics}
\centering
\begin{tabular}[t]{l|c|c|c|r}
\hline
 & Bukavu (N=1250) & Goma (N=1299) & Total (N=2549) & p value\\
\hline
\textbf{PreT4_Eff_WorldBank} &  &  &  & 0.226\\
\hline
~~~N-Miss & 540 & 348 & 888 & \\
\hline
~~~Mean (SD) & 4.697 (1.814) & 4.809 (1.888) & 4.761 (1.857) & \\
\hline
~~~Range & 1.000 - 7.000 & 1.000 - 7.000 & 1.000 - 7.000 & \\
\hline
\textbf{PreT4_Eff_DFID} &  &  &  & < 0.001\\
\hline
~~~N-Miss & 565 & 598 & 1163 & \\
\hline
~~~Mean (SD) & 5.012 (1.711) & 4.452 (1.875) & 4.729 (1.817) & \\
\hline
~~~Range & 1.000 - 7.000 & 1.000 - 7.000 & 1.000 - 7.000 & \\
\hline
\textbf{PreT4_Eff_INGO} &  &  &  & < 0.001\\
\hline
~~~N-Miss & 197 & 132 & 329 & \\
\hline
~~~Mean (SD) & 5.066 (1.596) & 5.349 (1.607) & 5.214 (1.607) & \\
\hline
~~~Range & 1.000 - 7.000 & 1.000 - 7.000 & 1.000 - 7.000 & \\
\hline
\textbf{PreT4_Eff_NatiGov} &  &  &  & < 0.001\\
\hline
~~~N-Miss & 211 & 173 & 384 & \\
\hline
~~~Mean (SD) & 3.162 (1.871) & 3.883 (1.736) & 3.537 (1.837) & \\
\hline
~~~Range & 1.000 - 7.000 & 1.000 - 7.000 & 1.000 - 7.000 & \\
\hline
\textbf{PreT4_Eff_ProvGov} &  &  &  & < 0.001\\
\hline
~~~N-Miss & 210 & 176 & 386 & \\
\hline
~~~Mean (SD) & 3.139 (1.868) & 3.787 (1.725) & 3.476 (1.824) & \\
\hline
~~~Range & 1.000 - 7.000 & 1.000 - 7.000 & 1.000 - 7.000 & \\
\hline
\textbf{PreT4_Eff_CityGov} &  &  &  & 0.001\\
\hline
~~~N-Miss & 205 & 188 & 393 & \\
\hline
~~~Mean (SD) & 3.275 (1.910) & 3.529 (1.700) & 3.406 (1.809) & \\
\hline
~~~Range & 1.000 - 7.000 & 1.000 - 7.000 & 1.000 - 7.000 & \\
\hline
\textbf{PreT4_Eff_Cong_Pub} &  &  &  & 0.086\\
\hline
~~~N-Miss & 273 & 199 & 472 & \\
\hline
~~~Mean (SD) & 3.508 (1.806) & 3.640 (1.698) & 3.578 (1.750) & \\
\hline
~~~Range & 1.000 - 7.000 & 1.000 - 7.000 & 1.000 - 7.000 & \\
\hline
\textbf{PreT4_Eff_EuroComp} &  &  &  & 0.289\\
\hline
~~~N-Miss & 474 & 335 & 809 & \\
\hline
~~~Mean (SD) & 4.472 (1.776) & 4.563 (1.801) & 4.522 (1.790) & \\
\hline
~~~Range & 1.000 - 7.000 & 1.000 - 7.000 & 1.000 - 7.000 & \\
\hline
\textbf{PreT4_Eff_ChinaSOE} &  &  &  & 0.995\\
\hline
~~~N-Miss & 212 & 210 & 422 & \\
\hline
~~~Mean (SD) & 4.656 (1.723) & 4.657 (1.682) & 4.656 (1.702) & \\
\hline
~~~Range & 1.000 - 7.000 & 1.000 - 7.000 & 1.000 - 7.000 & \\
\hline
\textbf{PreT4_Eff_CongComp} &  &  &  & 0.015\\
\hline
~~~N-Miss & 284 & 196 & 480 & \\
\hline
~~~Mean (SD) & 3.620 (1.871) & 3.811 (1.716) & 3.722 (1.792) & \\
\hline
~~~Range & 1.000 - 7.000 & 1.000 - 7.000 & 1.000 - 7.000 & \\
\hline
\end{tabular}
\end{table}


Call:
lm(formula = PosTre6_Brand_Satisfn ~ brand_pooled + ext_pooled + 
    brand_pooled * ext_pooled, data = d)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.6333 -0.6333  0.6553  0.7807  1.7807 

Coefficients:
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     5.633333   0.105039  53.631   <2e-16 ***
brand_pooledINGO               -0.347619   0.151177  -2.299   0.0216 *  
brand_pooledSOE                -0.300000   0.149988  -2.000   0.0456 *  
ext_pooledNeg                  -0.288652   0.149336  -1.933   0.0534 .  
ext_pooledPos                  -0.109104   0.150660  -0.724   0.4690    
brand_pooledINGO:ext_pooledNeg  0.274125   0.212938   1.287   0.1981    
brand_pooledSOE:ext_pooledNeg   0.241033   0.213508   1.129   0.2591    
brand_pooledINGO:ext_pooledPos  0.233082   0.214906   1.085   0.2782    
brand_pooledSOE:ext_pooledPos  -0.004931   0.213952  -0.023   0.9816    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.627 on 2063 degrees of freedom
  (477 observations deleted due to missingness)
Multiple R-squared:  0.006099,	Adjusted R-squared:  0.002245 
F-statistic: 1.582 on 8 and 2063 DF,  p-value: 0.1249


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Tue, Aug 08, 2023 - 09:20:56
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ 
\cline{2-3} 
\\[-1.8ex] & \multicolumn{2}{c}{PosTre6\_Brand\_Satisfn} \\ 
\\[-1.8ex] & (1) & (2)\\ 
\hline \\[-1.8ex] 
 brand\_pooledINGO & $-$0.35$^{*}$ (0.15) & $-$0.32$^{*}$ (0.15) \\ 
  brand\_pooledSOE & $-$0.30$^{*}$ (0.15) & $-$0.30$^{*}$ (0.15) \\ 
  ext\_pooledNeg & $-$0.29 (0.15) & $-$0.28 (0.15) \\ 
  ext\_pooledPos & $-$0.11 (0.15) & $-$0.12 (0.15) \\ 
  BG2\_Age &  & 0.003 (0.003) \\ 
  BG2\_Sex\_1Male &  & $-$0.02 (0.07) \\ 
  Dem3\_Edu\_if\_col &  & $-$0.18 (0.10) \\ 
  Dem3\_Occpn\_if\_desk &  & $-$0.05 (0.10) \\ 
  BG2\_CityGoma &  & $-$0.21$^{**}$ (0.07) \\ 
  Knowl9\_IMAGINE &  & 0.45$^{****}$ (0.08) \\ 
  act\_qrt &  & 0.23$^{**}$ (0.08) \\ 
  AddOut8\_KnowCh &  & 0.44$^{****}$ (0.11) \\ 
  PreT4\_Context\_PoliKnowle &  & 0.15$^{****}$ (0.03) \\ 
  brand\_pooledINGO:ext\_pooledNeg & 0.27 (0.21) & 0.23 (0.21) \\ 
  brand\_pooledSOE:ext\_pooledNeg & 0.24 (0.21) & 0.24 (0.21) \\ 
  brand\_pooledINGO:ext\_pooledPos & 0.23 (0.21) & 0.25 (0.21) \\ 
  brand\_pooledSOE:ext\_pooledPos & $-$0.005 (0.21) & 0.02 (0.21) \\ 
  Constant & 5.63$^{****}$ (0.11) & 5.05$^{****}$ (0.16) \\ 
 \hline \\[-1.8ex] 
Observations & 2,072 & 2,072 \\ 
R$^{2}$ & 0.01 & 0.06 \\ 
Adjusted R$^{2}$ & 0.002 & 0.05 \\ 
Residual Std. Error & 1.63 (df = 2063) & 1.59 (df = 2054) \\ 
F Statistic & 1.58 (df = 8; 2063) & 7.72$^{****}$ (df = 17; 2054) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{2}{r}{$^{*}$p$<$0.05; $^{**}$p$<$0.01; $^{***}$p$<$0.001} \\ 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Tue, Aug 08, 2023 - 09:20:57
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ 
\cline{2-3} 
\\[-1.8ex] & \multicolumn{2}{c}{PosTre6\_Brand\_Satisfn} \\ 
\\[-1.8ex] & (1) & (2)\\ 
\hline \\[-1.8ex] 
 brand\_pooledINGO & $-$0.30$^{*}$ (0.15) & $-$0.28 (0.15) \\ 
  brand\_pooledSOE & $-$0.28 (0.15) & $-$0.28 (0.15) \\ 
  ext\_pooledNeg & $-$0.25 (0.15) & $-$0.24 (0.14) \\ 
  ext\_pooledPos & $-$0.08 (0.15) & $-$0.11 (0.15) \\ 
  BG2\_Age &  & 0.003 (0.003) \\ 
  BG2\_Sex\_1Male &  & $-$0.01 (0.07) \\ 
  Dem3\_Edu\_if\_col &  & $-$0.21$^{*}$ (0.10) \\ 
  Dem3\_Occpn\_if\_desk &  & $-$0.08 (0.10) \\ 
  BG2\_CityGoma &  & $-$0.32 (0.23) \\ 
  Knowl9\_IMAGINE &  & 0.42$^{****}$ (0.09) \\ 
  act\_qrt &  & 0.57$^{*}$ (0.25) \\ 
  AddOut8\_KnowCh &  & 0.45$^{***}$ (0.12) \\ 
  PreT4\_Context\_PoliKnowle &  & 0.16$^{****}$ (0.03) \\ 
  factor(BG2\_Qrt)Cahi & 0.22 (0.26) & $-$0.05 (0.17) \\ 
  factor(BG2\_Qrt)Cimpunda & $-$0.30 (0.29) & $-$0.48$^{*}$ (0.21) \\ 
  factor(BG2\_Qrt)Himbi & $-$0.01 (0.29) & 0.15 (0.29) \\ 
  factor(BG2\_Qrt)Kahembe & $-$0.96 (0.58) & $-$0.73 (0.57) \\ 
  factor(BG2\_Qrt)Kajangu & $-$0.39 (0.27) & $-$0.07 (0.31) \\ 
  factor(BG2\_Qrt)Kasha-CBagira & 0.16 (0.25) & 0.60$^{*}$ (0.29) \\ 
  factor(BG2\_Qrt)Kasika & $-$0.14 (0.28) & 0.004 (0.28) \\ 
  factor(BG2\_Qrt)Katindo & 0.25 (0.35) & 0.30 (0.35) \\ 
  factor(BG2\_Qrt)Katoyi & 0.25 (0.27) & 0.28 (0.26) \\ 
  factor(BG2\_Qrt)Kyeshero & $-$0.26 (0.25) & 0.56$^{**}$ (0.19) \\ 
  factor(BG2\_Qrt)Les\_Volcans & 0.28 (0.35) & 0.43 (0.35) \\ 
  factor(BG2\_Qrt)Mabanga\_Nord & 0.21 (0.31) & 0.31 (0.31) \\ 
  factor(BG2\_Qrt)Mabanga\_Sud & $-$0.70$^{*}$ (0.32) & $-$0.55 (0.32) \\ 
  factor(BG2\_Qrt)Majengo & 0.01 (0.28) & 0.06 (0.27) \\ 
  factor(BG2\_Qrt)Mapendo & $-$0.16 (0.34) & $-$0.20 (0.34) \\ 
  factor(BG2\_Qrt)Mikeno & $-$0.25 (0.57) & $-$0.04 (0.57) \\ 
  factor(BG2\_Qrt)Mosala & 0.13 (0.27) & $-$0.02 (0.18) \\ 
  factor(BG2\_Qrt)Munigi I & $-$0.71$^{*}$ (0.32) & $-$0.59 (0.32) \\ 
  factor(BG2\_Qrt)Munigi II & 0.24 (0.27) & 0.27 (0.27) \\ 
  factor(BG2\_Qrt)Murara & 0.39 (0.35) & 0.38 (0.35) \\ 
  factor(BG2\_Qrt)Ndosho & $-$0.76$^{**}$ (0.26) &  \\ 
  factor(BG2\_Qrt)Nyakaliba & 0.08 (0.29) & $-$0.27 (0.21) \\ 
  factor(BG2\_Qrt)Nyatende & 0.24 (0.30) & $-$0.03 (0.22) \\ 
  factor(BG2\_Qrt)Panzi & 0.36 (0.23) &  \\ 
  factor(BG2\_Qrt)Virunga & 0.12 (0.35) & 0.26 (0.35) \\ 
  brand\_pooledINGO:ext\_pooledNeg & 0.23 (0.21) & 0.19 (0.21) \\ 
  brand\_pooledSOE:ext\_pooledNeg & 0.21 (0.21) & 0.21 (0.21) \\ 
  brand\_pooledINGO:ext\_pooledPos & 0.17 (0.21) & 0.23 (0.21) \\ 
  brand\_pooledSOE:ext\_pooledPos & $-$0.01 (0.21) & 0.03 (0.21) \\ 
  Constant & 5.60$^{****}$ (0.24) & 4.75$^{****}$ (0.30) \\ 
 \hline \\[-1.8ex] 
Observations & 2,072 & 2,072 \\ 
R$^{2}$ & 0.05 & 0.08 \\ 
Adjusted R$^{2}$ & 0.03 & 0.07 \\ 
Residual Std. Error & 1.60 (df = 2038) & 1.57 (df = 2031) \\ 
F Statistic & 3.26$^{****}$ (df = 33; 2038) & 4.69$^{****}$ (df = 40; 2031) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{2}{r}{$^{*}$p$<$0.05; $^{**}$p$<$0.01; $^{***}$p$<$0.001} \\ 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Tue, Aug 08, 2023 - 09:20:57
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ 
\cline{2-3} 
\\[-1.8ex] & \multicolumn{2}{c}{PosTre6\_Brand\_Satisfn} \\ 
\\[-1.8ex] & (1) & (2)\\ 
\hline \\[-1.8ex] 
 brand\_pooledINGO & $-$0.22 (0.14) & $-$0.19 (0.14) \\ 
  brand\_pooledSOE & $-$0.23 (0.14) & $-$0.22 (0.14) \\ 
  ext\_pooledNeg & $-$0.24 (0.14) & $-$0.24 (0.14) \\ 
  ext\_pooledPos & $-$0.07 (0.14) & $-$0.07 (0.14) \\ 
  BG2\_Age &  & 0.003 (0.003) \\ 
  BG2\_Sex\_1Male &  & $-$0.01 (0.07) \\ 
  Dem3\_Edu\_if\_col &  & $-$0.11 (0.10) \\ 
  Dem3\_Occpn\_if\_desk &  & $-$0.04 (0.10) \\ 
  BG2\_CityGoma &  & $-$0.75$^{**}$ (0.27) \\ 
  Knowl9\_IMAGINE &  & 0.63$^{****}$ (0.09) \\ 
  act\_qrt &  & 0.33$^{****}$ (0.08) \\ 
  AddOut8\_KnowCh &  & 0.32$^{**}$ (0.12) \\ 
  PreT4\_Context\_PoliKnowle &  & 0.09$^{**}$ (0.03) \\ 
  factor(enumid)2 & $-$1.35$^{****}$ (0.27) & $-$1.56$^{****}$ (0.26) \\ 
  factor(enumid)3 & $-$1.57$^{****}$ (0.27) & $-$1.62$^{****}$ (0.27) \\ 
  factor(enumid)4 & $-$1.20$^{****}$ (0.27) & $-$1.35$^{****}$ (0.27) \\ 
  factor(enumid)5 & $-$0.45 (0.27) & $-$0.29 (0.26) \\ 
  factor(enumid)6 & $-$0.40 (0.28) & $-$0.33 (0.27) \\ 
  factor(enumid)7 & 0.64$^{*}$ (0.27) & 0.61$^{*}$ (0.27) \\ 
  factor(enumid)8 & 0.31 (0.28) & 0.41 (0.28) \\ 
  factor(enumid)9 & $-$0.11 (0.28) & $-$0.37 (0.28) \\ 
  factor(enumid)10 & $-$1.84$^{****}$ (0.27) & $-$2.02$^{****}$ (0.27) \\ 
  factor(enumid)11 & 0.01 (0.27) & $-$0.38 (0.28) \\ 
  factor(enumid)12 & $-$0.30 (0.27) & $-$0.52$^{*}$ (0.26) \\ 
  factor(enumid)13 & $-$0.50 (0.30) & $-$0.57 (0.29) \\ 
  factor(enumid)14 & $-$1.00$^{***}$ (0.28) & $-$1.01$^{***}$ (0.28) \\ 
  factor(enumid)15 & $-$0.50 (0.29) & $-$0.47 (0.29) \\ 
  factor(enumid)16 & $-$0.76$^{**}$ (0.27) & $-$0.82$^{**}$ (0.27) \\ 
  factor(enumid)17 & $-$1.38$^{****}$ (0.27) & $-$0.62$^{*}$ (0.27) \\ 
  factor(enumid)18 & $-$1.17$^{****}$ (0.27) & $-$0.43 (0.27) \\ 
  factor(enumid)19 & $-$0.50 (0.27) & 0.30 (0.26) \\ 
  factor(enumid)20 & $-$0.62$^{*}$ (0.28) & 0.16 (0.27) \\ 
  factor(enumid)21 & $-$1.02$^{***}$ (0.26) & $-$0.29 (0.26) \\ 
  factor(enumid)22 & $-$0.40 (0.28) & 0.32 (0.27) \\ 
  factor(enumid)23 & $-$1.00$^{***}$ (0.26) & $-$0.10 (0.26) \\ 
  factor(enumid)24 & $-$0.25 (0.28) & 0.43 (0.28) \\ 
  factor(enumid)25 & $-$0.87$^{**}$ (0.28) & $-$0.08 (0.27) \\ 
  factor(enumid)26 & $-$0.50 (0.26) & 0.27 (0.26) \\ 
  factor(enumid)27 & $-$0.97$^{***}$ (0.26) & $-$0.23 (0.26) \\ 
  factor(enumid)28 & $-$1.62$^{****}$ (0.26) & $-$0.81$^{**}$ (0.26) \\ 
  factor(enumid)29 & $-$0.76$^{**}$ (0.29) & 0.07 (0.28) \\ 
  factor(enumid)30 & $-$0.49 (0.28) & 0.35 (0.27) \\ 
  factor(enumid)31 & $-$1.20$^{****}$ (0.29) & $-$0.31 (0.29) \\ 
  factor(enumid)32 & $-$0.73$^{**}$ (0.28) &  \\ 
  brand\_pooledINGO:ext\_pooledNeg & 0.19 (0.20) & 0.16 (0.20) \\ 
  brand\_pooledSOE:ext\_pooledNeg & 0.20 (0.20) & 0.22 (0.20) \\ 
  brand\_pooledINGO:ext\_pooledPos & 0.10 (0.21) & 0.11 (0.20) \\ 
  brand\_pooledSOE:ext\_pooledPos & $-$0.02 (0.20) & 0.01 (0.20) \\ 
  Constant & 6.29$^{****}$ (0.22) & 5.61$^{****}$ (0.24) \\ 
 \hline \\[-1.8ex] 
Observations & 2,072 & 2,072 \\ 
R$^{2}$ & 0.12 & 0.17 \\ 
Adjusted R$^{2}$ & 0.11 & 0.15 \\ 
Residual Std. Error & 1.54 (df = 2032) & 1.50 (df = 2024) \\ 
F Statistic & 7.29$^{****}$ (df = 39; 2032) & 8.99$^{****}$ (df = 47; 2024) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{2}{r}{$^{*}$p$<$0.05; $^{**}$p$<$0.01; $^{***}$p$<$0.001} \\ 
\end{tabular} 
\end{table} 

Call:
glm(formula = sat ~ TreatGroup, family = binomial(link = "logit"), 
    data = d.noext)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8544   0.6284   0.6284   0.7348   0.7756  

Coefficients:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)              1.5220     0.1683   9.042   <2e-16 ***
TreatGroupChinaControl  -0.4747     0.2255  -2.105   0.0353 *  
TreatGroupINGOControl   -0.3506     0.2303  -1.522   0.1279    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 740.17  on 694  degrees of freedom
Residual deviance: 735.41  on 692  degrees of freedom
AIC: 741.41

Number of Fisher Scoring iterations: 4


Call:
glm(formula = sat ~ TreatGroup + BG2_Age + BG2_Sex_1 + Dem3_Edu_lev + 
    Dem3_Occpn_if_desk + BG2_City + Knowl9_IMAGINE + act_qrt + 
    AddOut8_KnowCh + PreT4_Context_PoliKnowle, family = binomial(link = "logit"), 
    data = d.noext)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5347   0.2551   0.5340   0.7331   1.3586  

Coefficients:
                                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)                         -0.211372   0.479729  -0.441 0.659497    
TreatGroupChinaControl              -0.499477   0.237730  -2.101 0.035638 *  
TreatGroupINGOControl               -0.317300   0.241950  -1.311 0.189714    
BG2_Age                              0.020678   0.008309   2.489 0.012827 *  
BG2_Sex_1Male                       -0.456283   0.201618  -2.263 0.023629 *  
Dem3_Edu_levhigh school              0.366246   0.279252   1.312 0.189681    
Dem3_Edu_levother                   14.237519 636.619314   0.022 0.982157    
Dem3_Edu_levprimary school or less   0.180327   0.346996   0.520 0.603286    
Dem3_Occpn_if_desk                   0.212295   0.291873   0.727 0.467009    
BG2_CityGoma                        -0.226495   0.205544  -1.102 0.270493    
Knowl9_IMAGINE                       0.963596   0.282897   3.406 0.000659 ***
act_qrt                              0.721509   0.207083   3.484 0.000494 ***
AddOut8_KnowCh                       0.656058   0.401415   1.634 0.102182    
PreT4_Context_PoliKnowle             0.221748   0.091579   2.421 0.015461 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 740.17  on 694  degrees of freedom
Residual deviance: 666.81  on 681  degrees of freedom
AIC: 694.81

Number of Fisher Scoring iterations: 14


% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Tue, Aug 08, 2023 - 09:20:57
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ 
\cline{2-3} 
\\[-1.8ex] & \multicolumn{2}{c}{sat} \\ 
\\[-1.8ex] & (1) & (2)\\ 
\hline \\[-1.8ex] 
 TreatGroupChinaControl & $-$0.47$^{*}$ (0.23) & $-$0.50$^{*}$ (0.24) \\ 
  TreatGroupINGOControl & $-$0.35 (0.23) & $-$0.32 (0.24) \\ 
  BG2\_Age &  & 0.02$^{*}$ (0.01) \\ 
  BG2\_Sex\_1Male &  & $-$0.46$^{*}$ (0.20) \\ 
  Dem3\_Edu\_levhigh school &  & 0.37 (0.28) \\ 
  Dem3\_Edu\_levother &  & 14.24 (636.62) \\ 
  Dem3\_Edu\_levprimary school or less &  & 0.18 (0.35) \\ 
  Dem3\_Occpn\_if\_desk &  & 0.21 (0.29) \\ 
  BG2\_CityGoma &  & $-$0.23 (0.21) \\ 
  Knowl9\_IMAGINE &  & 0.96$^{***}$ (0.28) \\ 
  act\_qrt &  & 0.72$^{***}$ (0.21) \\ 
  AddOut8\_KnowCh &  & 0.66 (0.40) \\ 
  PreT4\_Context\_PoliKnowle &  & 0.22$^{*}$ (0.09) \\ 
  Constant & 1.52$^{****}$ (0.17) & $-$0.21 (0.48) \\ 
 \hline \\[-1.8ex] 
Observations & 695 & 695 \\ 
Log Likelihood & $-$367.70 & $-$333.41 \\ 
Akaike Inf. Crit. & 741.41 & 694.81 \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{2}{r}{$^{*}$p$<$0.05; $^{**}$p$<$0.01; $^{***}$p$<$0.001} \\ 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Tue, Aug 08, 2023 - 09:20:57
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
\cline{2-5} 
\\[-1.8ex] & \multicolumn{2}{c}{PosTre6\_Brand\_Eff\_EuroNGO} & \multicolumn{2}{c}{PosTre6\_Brand\_Eff\_ChinaSOE} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4)\\ 
\hline \\[-1.8ex] 
 brand\_pooledINGO & $-$0.20 (0.14) & $-$0.27$^{*}$ (0.13) & 0.005 (0.14) & $-$0.11 (0.14) \\ 
  brand\_pooledSOE & $-$0.26 (0.14) & $-$0.26$^{*}$ (0.13) & $-$0.03 (0.14) & $-$0.04 (0.14) \\ 
  ext\_pooledNeg & $-$0.34$^{*}$ (0.14) & $-$0.42$^{**}$ (0.13) & $-$0.11 (0.14) & $-$0.15 (0.13) \\ 
  ext\_pooledPos & $-$0.12 (0.14) & $-$0.26 (0.13) & 0.09 (0.14) & 0.12 (0.14) \\ 
  PreT4\_Eff\_INGO &  & 0.32$^{****}$ (0.02) &  &  \\ 
  PreT4\_Eff\_ChinaSOE &  &  &  & 0.28$^{****}$ (0.02) \\ 
  BG2\_Age &  & 0.001 (0.002) &  & $-$0.003 (0.003) \\ 
  BG2\_Sex\_1Male &  & $-$0.08 (0.07) &  & $-$0.04 (0.07) \\ 
  Dem3\_Edu\_if\_col &  & $-$0.13 (0.09) &  & $-$0.09 (0.09) \\ 
  Dem3\_Occpn\_if\_desk &  & 0.15 (0.09) &  & $-$0.05 (0.09) \\ 
  BG2\_CityGoma &  & 0.28$^{****}$ (0.07) &  & 0.15$^{*}$ (0.07) \\ 
  Knowl9\_IMAGINE &  & 0.10 (0.07) &  & 0.23$^{**}$ (0.08) \\ 
  act\_qrt &  & $-$0.01 (0.08) &  & 0.16$^{*}$ (0.08) \\ 
  AddOut8\_KnowCh &  & 0.24$^{*}$ (0.10) &  & 0.35$^{***}$ (0.10) \\ 
  PreT4\_Context\_PoliKnowle &  & 0.08$^{**}$ (0.03) &  & 0.08$^{**}$ (0.03) \\ 
  brand\_pooledINGO:ext\_pooledNeg & 0.29 (0.20) & 0.33 (0.19) & $-$0.05 (0.19) & 0.06 (0.19) \\ 
  brand\_pooledSOE:ext\_pooledNeg & 0.31 (0.20) & 0.37$^{*}$ (0.19) & $-$0.02 (0.19) & 0.02 (0.19) \\ 
  brand\_pooledINGO:ext\_pooledPos & 0.10 (0.20) & 0.35 (0.19) & $-$0.30 (0.20) & $-$0.12 (0.20) \\ 
  brand\_pooledSOE:ext\_pooledPos & 0.15 (0.20) & 0.20 (0.19) & $-$0.15 (0.19) & $-$0.23 (0.19) \\ 
  Constant & 5.28$^{****}$ (0.10) & 3.46$^{****}$ (0.18) & 5.07$^{****}$ (0.10) & 3.61$^{****}$ (0.18) \\ 
 \hline \\[-1.8ex] 
Observations & 2,054 & 1,788 & 2,053 & 1,705 \\ 
R$^{2}$ & 0.004 & 0.16 & 0.004 & 0.15 \\ 
Adjusted R$^{2}$ & 0.0000 & 0.16 & $-$0.0002 & 0.14 \\ 
Residual Std. Error & 1.53 (df = 2045) & 1.32 (df = 1769) & 1.47 (df = 2044) & 1.33 (df = 1686) \\ 
F Statistic & 1.00 (df = 8; 2045) & 19.22$^{****}$ (df = 18; 1769) & 0.95 (df = 8; 2044) & 16.36$^{****}$ (df = 18; 1686) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{4}{r}{$^{*}$p$<$0.05; $^{**}$p$<$0.01; $^{***}$p$<$0.001} \\ 
\end{tabular} 
\end{table} 

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Tue, Aug 08, 2023 - 09:20:57
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lcccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
\cline{2-5} 
\\[-1.8ex] & \multicolumn{2}{c}{ingo\_eff} & \multicolumn{2}{c}{soe\_eff} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4)\\ 
\hline \\[-1.8ex] 
 TreatGroupCooperationNegative & $-$0.45$^{*}$ (0.20) & $-$0.51$^{*}$ (0.21) & 0.02 (0.20) & $-$0.01 (0.21) \\ 
  TreatGroupCooperationPositive & $-$0.20 (0.21) & $-$0.26 (0.22) & 0.13 (0.20) & 0.05 (0.21) \\ 
  BG2\_Age &  & $-$0.02$^{**}$ (0.01) &  & $-$0.02$^{**}$ (0.01) \\ 
  BG2\_Sex\_1Male &  & 0.02 (0.18) &  & 0.38$^{*}$ (0.18) \\ 
  Dem3\_Edu\_levhigh school &  & 0.45 (0.26) &  & 0.52$^{*}$ (0.25) \\ 
  Dem3\_Edu\_levother &  & 0.85 (1.13) &  & $-$0.51 (0.82) \\ 
  Dem3\_Edu\_levprimary school or less &  & 0.37 (0.32) &  & 0.38 (0.31) \\ 
  Dem3\_Occpn\_if\_desk &  & 0.58$^{*}$ (0.30) &  & $-$0.02 (0.27) \\ 
  BG2\_CityGoma &  & 0.40$^{*}$ (0.18) &  & 0.15 (0.18) \\ 
  Knowl9\_IMAGINE &  & 0.14 (0.21) &  & 0.63$^{**}$ (0.22) \\ 
  act\_qrt &  & 0.04 (0.20) &  & 0.34 (0.20) \\ 
  AddOut8\_KnowCh &  & 0.64$^{*}$ (0.30) &  & 0.61$^{*}$ (0.31) \\ 
  PreT4\_Context\_PoliKnowle &  & 0.22$^{**}$ (0.08) &  & 0.20$^{*}$ (0.08) \\ 
  Constant & 1.07$^{****}$ (0.15) & 0.62 (0.45) & 0.76$^{****}$ (0.14) & $-$0.15 (0.44) \\ 
 \hline \\[-1.8ex] 
Observations & 695 & 695 & 692 & 692 \\ 
Log Likelihood & $-$421.64 & $-$398.92 & $-$427.75 & $-$404.56 \\ 
Akaike Inf. Crit. & 849.28 & 825.84 & 861.51 & 837.11 \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{4}{r}{$^{*}$p$<$0.05; $^{**}$p$<$0.01; $^{***}$p$<$0.001} \\ 
\end{tabular} 
\end{table} 
